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The multivariate Hotelling's T² control chart is designed to be used in a mass production for processes where data to estimate the mean vector and covariance matrix as well as the computation of control limits are available before a production run. Recent years have seen a trend in manufacturing industries to produce smaller lot sizes, a.k.a., low volume production which is a result of increased importance given to just-in-time (JIT) manufacturing techniques, synchronous manufacturing and the reduction of in-process inventory and costs. This new manufacturing environment is also referred to as short runs production or short runs. In a short runs environment, it is difficult or perhaps impossible to establish a reliable historical data set in setting valid control limits and in estimating process parameters due to the availability of insufficient data for a particular process because production runs are usually short and change frequently from one process to another. There is also a need to start charting at or very near the beginning of the run in such a case. Another problem encountered in a short runs production such as in job shops is that there are many different types of measurements so that many different control charts are needed. Standardized control charts that allow different statistics to be plotted on the same chart are extremely useful in short runs. Control charts with standard scale simplify the control charting process in a short runs environment. In this paper, we address the multivariate short runs problems for process dispersion based on individual measurements by presenting the required formulas so that the chart can be used from the start of production, whether or not prior information for estimating the chart's limit and its parameter is available. The proposed chart plots standardized statistics for multiple parts on the same chart. This paper extends the work of the authors in Ref. 13.

The current trend in modern production is directed towards shorter and shorter production runs. The two major reasons causing this trend are the rapid spread of the just in time (JIT) philosophy and the constantly increasing multiplicity of customer demands. The short runs of modern production not only constitute a challenge for production management, but they also cause some problems when applying traditional statistical methods, designed to be used for large sets of data. One of these methods is process capability studies. Since theories on how to use process capability studies in short production environments are incomplete, the aim of this paper is to present some ideas which will partly fill this gap.

The theories of process capability studies for short runs presented are based on ideas of focusing on the process, not on the products, and on using data transformation. By using the transformation presented, it is possible to conduct process capability studies in a traditional straightforward manner. A simulation study shows that the suggested transformation technique works satisfactorily in real situations. Finally, the -plot is introduced as a method of interpreting and analyzing the capability of a short run production process. By using the -plot, additional information is obtained concerning the capability of a process, compared to using traditional process capability indices only.